Disruptive Innovation for Pragmatic Interoperability March 1, 2016

Transcription

1 Disruptive Innovation for Pragmatic Interoperability March 1, 2016 David Waltman Chief Information Strategy Officer, VHA

2 Conflict of Interest David Waltman Has no real or apparent conflicts of interest to report.

3 Agenda DoD and VA Interoperability Overview Types of Interoperability and Semantic Interoperability examples Demonstration of practical applications

4 Learning Objectives Understand VA s application of semantic interoperability Understand VA innovations for applying semantic interoperability at the point of care

5 History of DoD Electronic Record & Interoperability Implementation Paper DoD Conversions to Digital Records Ancillary Data Inpatient Clinical Documentation Outpatient Clinical Documentation Theater Data Miscellaneous Documentation, Radiology, STRs Digital older None Comprehensiveness for Sources and Data Domains FHIE BHIE CHDR Image Sharin g VLER DMI X DMI X National Standard Terminology Completeness Full DoD-VA Interoperability 5

6 VA-DoD Interoperability Data Sources Community Data DoD New EHR Data Dental, Accessions Ancillary Data Outpatient Clinical Documentation DoD Providers JLV Inpatient Clinical Documentation Theater Data DHMSM CHCS AHLTA CDR Essentris TMDS HAIMS DMIX Data Exchange Service (DES) EMI DAS & CVIX VLER VistA Exchange VistA VistA Imaging VA Data ehmp JLV VA VA Providers VBA Personnel Misc. Documentation, Radiology Images, STRs VBMS Some details simplified; JLV not yet connected to VistA Exchange. Currently uses VistA Data Services and DoD Relay Service 6 (not shown)

7 What is the Service Treatment Record? DoD instruction defines the Service Treatment Record (STR) as: All essential medical, dental, and mental health documentation required to ensure continued medical, dental, and mental health care given to a Service member during their military service The Service Treatment Record is a subset of All Medical Documentation Available for a Service member 7

8 Service Treatment Record Scope ALL MEDICAL DOCUMENTATION SERVICE TREATMENT RECORD Platform IT Raw Data Reports AHLTA Reports Encounter Notes, Problem list, Lab, Rad, Pharm, Encounter history, Consults, Clinical Notes Radiology Images CLRs Mental Health Notes Discharge Summaries Network Care Psychotherapy Notes Inpatient Admissions 8

9 JLV STR coverage mid 2016 SERVICE TREATMENT RECORD AHLTA Encounter Notes, Problem list, Lab, Rad, Pharm, Encounter history, Consults, Clinical Notes Psychotherapy Notes JLV 9

10 Three Key Interoperability Relationships Relationship #1 Technical Interoperability Information must be reliably exchanged between multiple Systems so People in different places and organizations can see it, use it and add to it Relationship #2 Semantic Interoperability Information exchanged between Systems must have shared meaning in order for People (assisted by computers) to use it for making care and benefits decisions Relationship #3 Process Interoperability People (assisted by computers) must be able to make use of shared Information in their local workflows and processes for providing and receiving care and benefits (Source: DoD/VA IPO HDIMP) 10

11 Seamless Interoperability for Care and Benefits Technical Interoperability Information Exchange Semantic Interoperability Shared Meaning Process Interoperability Workflow Continuity Seamless Interoperability is Technical, Semantic and Process Interoperability together 11

12 Creating a Seamless Record for SFC (Ret) Smith SFC (Ret) Smith s health record includes information stored in several systems DoD VITALS - Madigan AMC DoD LAB RESULT Walter Reed NMMC DOWTOWN ORTHO CONSULT San Antonio, TX DoD MEDICATION San Antonio MMC 2006 Madigan CHCS Downtown Ortho AHLTA (CDR) Walter Reed CHCS VA LAB RESULT San Antonio VA 2007 VA MEDICATION Richmond VA 2009 VA VITALS Puget Sound VA 2012 San Antonio VistA Richmond VistA PROVIDENCE HOSPITAL CONSULT Seattle, WA 2012 (plus hundreds more pieces of data like those above) Providence Hospital Puget Sound VistA The goal of Seamless Interoperability is to share SFC Smith s information between systems, while ensuring all systems understand what the information means 12

13 Semantic Interoperability Basics Semantically Interoperable data is both exchanged between systems and standardized (also called normalized ), so systems know whether data has the same meaning DoD Local Name Temperature Pulse Hgb A1c FPG VA Local Name Temp Heart Rate GlycHemg Plasma Glucose Private Sector System Name T. HR GlycHgb Pl. Glucose Mapped to National Standard Codes Standard Name Body temperature Heart rate Hemoglobin A1c Fasting Glucose in Serum or plasma 13

14 Standardization and Correlation Standardized data is information known to have shared meaning Correlations are the meaningful associations between pieces of information which allow data from different systems to be directly compared Computers use metadata (data about data, such as Codes) to determine if information from different systems has shared meaning Direct Correlation is simple, one to one exact match associations between pieces of information Knowledge Based Correlation uses more sophisticated analysis of metadata to determine if meaningful associations can be made between pieces of information. May include rules and inference engines, artificial intelligence, and computer based expert systems 14

15 Direct Correlation codes have 1 for 1 exact matches Data from different systems may share exactly matched codes, as in the example below DoD Local Name Name (LOINC) Code (LOINC) Value Body Height Body Height cm VA Local Name Name (LOINC) Code (LOINC) Value Height Body Height IN The data above can be directly correlated, because both entries share the same code (LOINC in this example). Even though the local names are different, computers can treat the data as if both entries came from the same system. Computers know these two pieces of data represent the same concept since they share the same code 15

16 But wait, there s more Taking a closer look, we see values recorded in different units (centimeters and inches) between the two systems DoD Local Name Name (LOINC) Code (LOINC) Value (units) Body Height Body Height (cm) VA Local Name Name (LOINC) Code (LOINC) Value Height Body Height (IN) The code set for Units of Measure (the Unified Code for Units of Measure ), or UCUM, is not present, and is not implemented in VA and DoD. This means computers can t tell if centimeters and inches mean the same thing, or how to compare them So even though there is direct correlation of the concept, these items cannot be directly compared (graphed together) because the units of measure (cm or inches) have not been standardized. 16

17 Knowledge-Based Correlation (vital signs example) Sometimes data can be correlated even though there is not an exact match between codes if we have knowledge about how they are related DoD Local Name Heart Rate Name Heart rate Peripheral artery by palpation -- resting Code Value BPM VA Local Name Pulse Name Heart rate Peripheral artery by palpation Code Value BPM While these two entries have two different LOINC codes (one to specifically indicate resting heart rate), they may be sufficiently similar concepts to have useful shared meaning A knowledge-based software system can apply rules which allow these entries to be compared for the purpose of graphing, for example, because it knows both concepts are sufficiently similar that it is safe to compare them for certain purposes 17

18 Knowledge-Based Correlation (medication example) Sometimes data shares practically identical meaning, even though there is not an exact match between codes. DoD Local Name Name Code Synthroid 25mcg Tab, Oral Levothyroxine Sodium 25mcg, (Synthroid), Tablet, Oral VA Local Name Name Code Levothyroxine Sodium 25mcg, TAB Oral Levothyroxine Sodium MG Oral Tablet While these two entries have two different codes (RxNorm), they may still have shared meaning. In this instance, the DoD local name is coded to the brand name version of the drug ( Synthroid ), while the VA term is mapped to the generic version of the drug ( Levothyroxine ). No Direct Correlation can be made, even though both drugs share the same active ingredient, same form, and same dose. A knowledge-based software system, however, could apply rules which allow these entries to be correlated for certain purposes. In fact, several commercially available Drug Interaction Checking tools are widely used for this and other clinical purposes (e.g. drugdrug and drug-allergen adverse reaction checking). 18

19 Knowledge-Based Correlation (Document Type example) Sometimes data can be correlated even though there is not an exact match between codes. DoD Local Name Name Code Consult Consult (setting and specialty unspecified) VLER Partner Local Name Name Code Derm Consult Dermatology Consult These two pieces of data have different LOINC codes representing different hierarchical levels of specificity. The DoD term is mapped to the parent document type, Consult, while the VA term is mapped to the child document type for consult that includes the specialty ( cardiology ). Knowledge based terminology software can apply knowledge of the LOINC Document Type Hierarchy to recognize that both of these documents are consults. Another filter could be applied to look for all documents that are associated with Dermatology (consults, progress notes, etc.) 19

20 Demonstrations of Applied Semantic Interoperability at the point of care Joint Legacy Viewer (VA and DoD) Standardized names/code view Sorting by standardized code Enterprise Health Management Platform (VA) Code-enhanced longitudinal record search Variable reference range display for graphs over time 20

21 Questions Presented by David Waltman Visit Booth in the Federal Health IT Solutions Pavilion Other VA Sessions at HIMSS: Today: Transforming VistA Evolution through Collaboration: 2:30 3:30 PM Wednesday, March 2: Enhanced Home Healthcare Coordination for Veterans: 1:00 2:00 PM VA s Data Sharing & Interoperability Strategies: 2:30 3:30 PM Evaluating Semantic Technology to Enhance EHR Visualization: 4:00 5:00 PM Thursday, March 3: VistA Evolution EHR Initiatives in : 8:30 9:30 AM Achieving Interoperability with VA & Private Sector Partners: 2:30 3:30 PM * * This is a DOD session on joint work.